IJMLC 2012 Vol.2(2): 138-143 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.102

The Study on Accurate Modeling of Suspension Based on ADAMS

Li Xueying, Yu Zhuoping, and Xiong Lu

Abstract—The torque vibration derived from in-wheel-motor transmits to body frame through suspension system without the absorption of mechanical transmission parts, which influenced the quality of the vehicle NVH. This paper aims to build an accurate suspension system model to analyze the vibration transmission property. A multi-rigid suspension model and a multi-flexible suspension model had been established respectively. The vibration characteristics of two models were simulated, furthermore the swept-sine exciting vertical force signal on wheel contact point were input on the simulation models to find the difference between rigid and flexible model. The simulation results show that: the multi-flexible model can more accurately reflect the vibration characteristics of the suspension system in the high frequency range, hence more applicable to the simulation analysis of in-wheel-motor electric vehicle suspension system vibration characteristics. Then the rubber bushing model was replaced with new empirical rubber bushing model, the inherent frequency and the frequency response functions were compared. The results show: The multi-flexible suspension model with new empirical rubber bushing model hasn’t notable influence to inherent frequency. However, it can reflect more peak values of frequency response functions and the transmissibility at every peak frequency are higher than the original multi-flexible suspension model.

Index Terms—Multi-flexible suspension model, multi-rigid suspension model, new empirical model of rubber bushing, vibration characteristics.

Authors are with the Automobile Engineering Department, University of Tongji, Shanghai, China (e-mail: sherry_lixueying@hotmail.com; yuzhuoping@mail.tongji.edu.cn; xiong_lu@mail.tongji.edu.cn).


Cite: Li Xueying, Yu Zhuoping, and Xiong Lu, "The Study on Accurate Modeling of Suspension Based on ADAMS," International Journal of Machine Learning and Computing vol. 2, no. 2, pp. 138-143, 2012.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net